Skill guide
MLOps
MLOps applies software delivery and operational discipline to data, model training, deployment, monitoring, and retraining.
Why this capability matters
A model only creates durable value when its data, versions, deployment, performance, and failure response are reproducible and observable.
What competent practice includes
Experiment tracking
Data and model pipelines
Containerised serving
Monitoring and drift detection
Retraining and release operations
Portfolio evidence
A reproducible training-to-deployment pipeline with tracked experiments, an endpoint, monitoring, and a redeployment runbook.
See public project briefsProfessional applications
These are fields of application, not guaranteed job or income outcomes.
Model deliveryProduction monitoringML platform workDeployment consultingOperational readiness
Courses that develop this skill
AI Operations
MLOps (Machine Learning Operations)
Turning trained models into reproducible, monitored, production-grade ML systems.
AI Operations
LLMOps
Operationalizing large language models specifically — prompt versioning, evaluation, cost, and deployment at scale.
AI Operations
AIOps
Using AI to automate IT operations itself — anomaly detection, alert correlation, and self-healing infrastructure.
Connect the skill to a market path
Learn how this capability fits inside a complete problem, proof, service, and delivery journey.